The images are from our “serial block face scanning electron microscope” and are of small groups of HeLa cells (a type of cancer cell). The electron microscope allows us to see objects down to a few billionths of a metre in size. Each cell is divided up into around 200-300 individual sections (like a loaf of sliced bread) so we can see the insides of the cell. To start with we hope to analyse around 50 cells, but as a core facility we have an endless supply of different types of data we’d like to analyse!

How do Zooniverse volunteers contribute to your research?

The Zooniverse volunteers help us with our “segmentation” task – tracing a line over the nuclear envelope, which is a very important membrane inside the cell that separates the nucleus from the rest of the cell.

What have been the biggest challenges in setting up your project?

One of the key advantages of the citizen science approach is that we can have several different volunteers analyse each image, allowing us to combine the results to get a statistical understanding of the data. Combining the volunteers’ work in the most effective way possible turns out to be pretty tricky though! Especially since our data is stored as lots of individual lines, meaning a simple average doesn’t really work.

What discoveries, and other outputs, has your project led to so far?

The first thing we wanted to make sure was that the data we get is good enough, which thankfully it looks like it is! Even that was perhaps a bit unexpected to some people! We’ve found it has been popular as an education and outreach tool too, with reports of teachers using it in their classes to teach about cells. We’ve also shown it off at an event at the Natural History Museum in London and a CRUK event in Manchester and spoken about it at several conferences around the world. We’re writing up the first journal article about it right now in fact!

Once you’ve finished collecting data, what research questions do you hope to be able to answer?

While electron microscopy produces amazingly detailed images, there are very few studies where data has been fully quantified at large scale, since it’s such a labour-intensive process at the moment. By measuring the shapes of each object (nucleus, mitochondria etc.) in different types of cell we can perform robust quantitative comparisons that are not currently possible. This sort of knowledge will help us to understand many different diseases, like cancer, malaria and tuberculosis, as well as help us find effective treatments for those diseases.

What’s in store for your project in the future?

There’s a lot more to come from Etch a cell! We have plenty more data that we’d like to analyse in the same way – tracing outlines – but for some other objects inside a cell we think a different approach might be easier and more effective. We’re working on a few options, and hopefully future iterations will be easier for people to work with on mobile devices. In parallel, we’re also building an artificial intelligence system to use the results from Etch a cell to enable us to train computers to be as good at the task as humans.

What are your favourite other citizen research projects and why?

Bash The Bug was released at the same time as Etch a cell and is a really great project. EyeWire is another electron microscopy project that has been groundbreaking in the field of “connectomics” – trying to understand how the cells in brains are connected together. Cancer Research UK has produced several different projects with a great deal of success, it was our initial interaction with their team that brought citizen science to our attention in the first place.

What guidance would you give to other researchers considering creating a citizen research project?

Have a play around with the project builder, setting up a basic project is really easy!

And finally, when not at work, where are we most likely to find you?

I try to get outdoors as much as possible, whether that’s walking, running or cycling.

This week’s guest blog post is from Dr Gemma Hall, who is leading a range of Zooniverse educational outreach initiatives in the UK. Read on to find out about the activities she led earlier this month during British Science Week.

– Helen

Penguins, Plastics, and Poo

Science Week, a week when we scientists gush about our favourite subject, attempt to explain to others what we do all day or just get plain messy with icky, sticky crowd-pleasing experiments. I think I successfully covered all these things during Science Week. And I have Zooniverse to thank for (most of) this.

I’m a STEM Ambassador (https://www.stem.org.uk), which means I do lots of science outreach. And I’m a huge Zooniverse fan. Whether it’s bashing bugs, elephant expeditioning or galaxy-gazing, I love that anyone with a computer/internet connection can help with real people-powered research, including children.

So, for Science Week, instead of just talking about what scientists do, I used Zooniverse to get primary school children being the scientists. Children like feeling important, so key to engaging them from the outset meant emphasising the importance of helping real researchers make the world a better place. Cue dramatic gasps and disbelieving looks all around the ICT Suite!

Children also like being able to relate to what they’re learning about. And so, I introduced them to the researchers they would be helping…

Penguins

Scene: Children transfixed by a PowerPoint presentation showing a picture of Dr Tom Hart, of Penguin Watch, wrapped up against the harsh Antarctic elements, surrounded by penguins.

Tom is a Penguinologist, and I almost had to stop the teachers stampeding out of the ICT Suite in an effort to re-train so they too can get such a great job title. The children, meanwhile, were more captivated by the wondrous site of the hundreds of penguins.

I told the class that Tom’s laboratory is the Antarctic and he wears hefty, cold weather gear rather than a white lab coat. He studies penguins because they give us a really good indication of the effects humans have on the Antarctic. Tom needs to keep an eye on the penguins across many Antarctic sites, all day, every day of the year. However, I continued, Tom can’t live in the Antarctic all year because it’s too harsh and he’d miss his family. He has cameras taking hundreds of photos every day and now has so many that he needs help to analyse them.

And with that, the children keenly set about tagging penguins: Adelies, King, Gentoos, Chinstraps; adults, chicks and eggs. They were careful to observe the behaviour of the penguins and their habitats, which both gave indications of whether the penguins might be incubating eggs or caring for chicks. They imagined what working in the harsh Antarctic environment would be like and they were intrigued about what penguins get up to in the night!

The energy in the classroom could have powered the computers the children were using! And the concentration levels and tagging skills were higher than I’ve seen many adults apply (sorry, Adults!). Furthermore, they asked if they could continue helping to spot penguins at home, so huge was their passion to help.

Scene: Children looking at a PowerPoint presentation picture of a beach, with Peter Kohler and Ellie McKay of The Plastic Tide flying their drone.

Plastics have been in the news lots recently and these children were very clued-up, so they needed little introduction to the plastic problem. Already sufficiently motivated to help clean up our planet, they were spurred on even more by hearing that the The Plastic Tide project is the official project of British Science Week and that it was featuring on Sky News and the BBC!

I explained that Peter and Ellie needed help to tag plastic or litter in beach pictures taken by drones. Tagging the pictures teaches a computer program to recognise plastic. The more pictures that are tagged, the better the program will become. Soon, computers will be able to find the plastics themselves, aiding the creation of a global inventory of marine plastic pollution.

The children set to the task with determination, but it soon became apparent that they were not all totally happy; some were frustrated that not all the images had plastics in. After a gentle reminder that we really shouldn’t be hoping to find plastics and that it’s better to have plastic-free beaches, they returned to the task, only to exclaim later that tagging plastics was making them angry. However, this time, they were annoyed that they had spotted so much plastic and litter. Among their finds we had shoes, old toys, many bottle tops, rope, plastic bags, scores of fragments and even an old, gnarled “danger” sign.

We calculated that in one class of 30 children, each child had tagged between 10 and 20 images, so all together they had helped tag an outstanding 300–600 images. With many other schools around the country also tagging plastics, no wonder the Science Week target to tag over 250 000 images was smashed within days. In fact, by the final day, The Plastic Tide had a record-breaking 1.5 million tags—6 times their original target! That equates to 290 000 a day or 800+ tags a minute!

I spoke to Peter at the end of Science Week and he was blown away by the energy and support:

“Science Week has been a huge boost to tagging. The more tags we get, the better the computer algorithm becomes at detecting plastic. Each tag could help find millions more of the same item and will help us clean up our beaches”.

Peter also confided that there are some very exciting announcements coming soon from The Plastic Tide, so keep an eye out for those, and KEEP TAGGING!

Poo

I bet you’re wondering where the “poo” in the title comes from? Well, that’s the icky sticky crowd-pleaser I was referring to at the start. Let’s just say that soggy Weetabix squeezed through nylon tights with a hole in the toe end is a really great way to demonstrate the intestine and how it results in… poo! And I’m also told that before you see a penguin colony in the Antarctic, you smell it first.

For a cleaner approach to science, use Zooniverse!

Get tagging!

Get your children tagging!

Help with REAL research and make the world a better place.

Dr Gemma Hall is a Science & Technology Writer and STEM Ambassador. She loves explaining complex things simply, and enthusing people about the importance of science to their everyday lives. Gemma is working to develop Zooniverse in schools, enabling young people to perform real research so that they better understand what scientists do.

Anabelle (fourth from left) and the on-the-ground research team at the research station in Gabon

Location: School of Geography and the Environment, University of Oxford, UK

What are your main research interests?

‘Elephant Expedition’ is part of my PhD research at the University of Oxford, which I started in 2015. Our research is about understanding how forest elephants affect the ecosystems they live in.

Forest elephants are extremely endangered, largely due to hunting for ivory. However, because they live in such mysterious and remote forests, we don’t know as much about them as we would like to. Learning more about these important and threatened animals is critical, as the better you understand an animals ecology the more effectively you can advocate for and plan its conservation.

In our study site in Gabon, and across Africa, valuable savanna habitat is being lost due to over-expanding forests as a result of human-induced global change. Normally, you wouldn’t think of growing forests as a threat, but savanna habitat is home to most of the remaining large mammals in Africa and performs many important ecosystem functions, including carbon storage, so loss of savannas is a global concern. Elephants are ecosystem engineers meaning that they have a disproportionately large impact on the ecosystems they live in. This gives then the unique potential to affect how much forest or savanna is in a landscape, so they can help protect savannas in the face of expanding forests. Most of the research on how elephants might do this has been done on bush elephants, which are a completely different species to the forest elephants of central Africa. Our research aims to remedy this by focusing on how forest elephants affect the forest and savanna balance of the landscape they live in.

In order to better understand forest elephants, we first need to know where they are, so we’ve set up a network of hidden camera traps to photograph them as they move through the forest. Our 40 camera traps are attached to trees and take a photo when triggered by motion or heat. They are super useful for monitoring dangerous and elusive animals like forest elephants because they function 24/7 and can give us a really good idea about where in the landscape the elephants are spending their time without us having to disturb the elephants by following them on foot. This is where the citizen scientists come in – because the camera traps are quite sensitive they don’t only capture images of elephants, but also gorillas, chimpanzees, buffalo, antelope, or even passing birds and bats. The citizen scientists help us to classify all the images into categories based on what’s in them. We can then convert these classifications into data about where the elephants are at what times of year, and link it with our other environmental measurements to draw conclusions. What the citizen scientists contribute is absolutely essential to the research, and forms the backbone of everything we do.

Who else is in your project team? What are their roles?

Yadvinder Malhi (Oxford), Imma Oliveras (Oxford), William Bond (University of Cape Town), and Kate Abenermethy (University of Stirling) supervise me; and Josue Edzang-Ndong (ANPN Gabon) and David Lehmann (ANPN Gabon, University of Stirling) and Kathryn Jeffery (ANPN Gabon, University of Stirling) help managed the project on the ground in Gabon. A special mention should be made to @melvinosky and @jwidness, our wonderful project moderators.

Tell us more about the data used in your project

We have 40 motion and heat sensitive cameras set up along rainforest edges in Gabon, they take photos of all passing animals (mostly elephants, but also a lot of gorillas, chimpanzees, buffalo, leopard, and red river hogs!). These are the images that the volunteers help to classify.

How do Zooniverse volunteers contribute to your research?

In our project, volunteers are shown an image from one of our camera traps and they have to classify it according to what animal is in it. If the image contains a forest elephant, they also have to count how many elephants they see. The project is simple, so volunteers of all ages and skill levels can join, plus they can classify hundreds of images and therefore get lots of opportunities to spot cool animals.

The project’s feasibility relies on citizen scientists – from our network of hidden camera traps in the rainforest of Gabon we have nearly 2 million photographs we need to analyse and this would be impossible without the help of our dedicated volunteers. To date, there are 10,000 citizen scientists signed up on our website from all parts of the world, as long as you have an internet connection you can join the team.

Citizen science is wonderful because everybody benefits. As researchers we can process very large data sets (like our set of elephant photos) by harnessing the power of thousands of minds all working towards a common goal. This enables us to expand our research scope far beyond what would be possible as individuals – it’s the ultimate global collaboration. The citizen scientists benefit too. Volunteers are exposed to experiences that they might not otherwise have access to, for example in Elephant Expedition you essentially go on a virtual safari through the central African rainforest looking for forest elephants, gorillas, chimpanzees, leopards or mandrills (a type of monkey) – this just isn’t something most people will ever get the chance to do in real life. The project also has a vibrant online community of volunteers. One of the volunteers is a cancer sufferer and she says that participating in our project allows her to not be excluded from doing something just because she’s sick, it gives her a way to pass the time in hospital and makes her feel part of something meaningful.

Since we have so many camera traps and they are highly sensitive, we have many photographs – nearly 2 million! The photographs have a time and location stamp, so each time a volunteer classifies an image as having an elephant in it we know when and where that elephant was sighted. This information from the volunteers is synthesised and is what we’re using to build a time series of elephant habitat use across the landscape. Without the volunteers we would have no way of analysing the images, and therefore no data with which to answer our research questions. Citizen scientists play an integral role in the success of the project, the bottom line is that without them the project wouldn’t be able to work.

What have been the biggest challenges in setting up your project?

It isn’t really a challenge, more a learning journey. I think the amount of time it takes was a challenge, that you always have to be connected to answer questions and see to issues, and of course just learning how to manage such massive data sets has been a steep learning curve! It’s been great though, and I’ve been really humbled by the experience, because all of the volunteers on the project are so lovely and helpful it’s been amazing to be a part of.

What discoveries, and other outputs, has your project led to so far?

We haven’t started doing data analysis yet but we are very excited to see the results! We will be keeping all the volunteers updated on the project page as things continue.

What’s in store for your project in the future?

We have one more small run of final photos, and then we will begin the data analysis and writing up some research! It’s all very exciting and should be coming together in the next few months.

What are your favourite other citizen research projects and why?

Oh! I loved Snapshot Serengetti!

What guidance would you give to other researchers considering creating a citizen research project?

The potential for citizen science research is truly astounding. The world is a big place and the internet is able to connect us with one another. There are millions of potential volunteers across the globe who care as much about what you are researching as you do, and citizen science is an amazing way to connect with them. The best way to make a project effective is to find clever ways of linking volunteers and researchers according to the research interests of both. I think project effectiveness can also be measured by what both researchers and volunteers gain, for example did the research fulfil its scientific aims? Was the scope of the research enhanced by being able to use a global network of volunteers? Did the volunteers feel they gained some enjoyment and knowledge from the process of engaging with it? Would volunteers educate those around them about the research?

When designing a citizen science project, we found it most important to always remember that the people who volunteer to help you are smart and they care about what you’re researching. By including them in the project they become a part of the project, so always appropriately respect their time and skills. Our project depends on people sacrificing time out of their lives to help reach a research goal, so we always make sure we put in the time to communicate with volunteers, answer questions, and just generally engage personally with the people who make the project possible.

And finally, when not at work, where are we most likely to find you?

In Oxford, writing my thesis or destressing with some yoga, or maybe at home in Cape Town, South Africa, walking on the mountain or swimming in the sea. I also love to take road-trips across Southern Africa, there’s always something beautiful to see!

Below is a guest post from Robbie Parks, a PhD student from Imperial College London who is studying how global climate change is influencing human mortality.

Read on to learn more about how crowd sourcing via platforms such as the Zooniverse can help us prepare for, and respond to, extreme weather.

– Helen

The future of extreme weather forecasting, preparation and response

Robbie Parks

The extreme weather around the globe during 2017 was a grim reminder of how truly devastating extreme weather can be. The citizens of Houston, New Orleans, Mumbai, Bangladesh, Nepal, Puerto Rico, the Dominican Republic, Florida and Sierra Leone joined the swelling ranks of victims from hurricanes and floods. Hurricane Harvey was followed by the powerful storms Irma, Jose, and Maria. Heatwaves and drought around the world, from India to Italy, have also brought misery and even death to millions.

When extreme weather strikes, lives and livelihoods are ruined, infrastructure destroyed, landscapes altered (sometimes irreversibly), and communities are often left with long-term mental and physical trauma. This clearly is a challenge not just for developing countries, but also for highly industrialised nations like the United States. What can we do about this?

Beyond avoiding the most devastating future extreme weather by curbing greenhouse gases improving forecast capabilities and capacity provides some of the best defence. National and local decision makers require reliable warnings to be able to provide emergency planning and relief in their locally minded extreme weather action plans.

Nowadays, the most modern supercomputers, armed with a suite of state-of-the art models of atmospheric processes, generate higher and higher spatial and temporal resolutions with steadily improving forecasting skill. Progress in this ‘quiet revolution’ of forecasting is significant.1 But the next 10 years could yet see another huge paradigm shift in forecast capabilities.

A critical part of this drive for improved weather forecasting is the World Weather Research Programme’s (WWRP) 10-year HIWeather (High Impact Weather) project, a consortium of over 2000 scientists from diverse fields participating from institutions worldwide. I spent some time with Dr Paolo Ruti, global chief of the WWRP at the World Meteorological Organization (WMO) in Geneva, Switzerland, to discuss advances in forecasting extreme weather through HIWeather.

According to Dr Ruti, the ambition of HIWeather is “to promote cooperative international research to achieve a dramatic increase in resilience to high impact weather”. This will translate to saving more lives on the ground in the onset of an extreme weather event, both by improved forecasting capability and better communication to decision makers who activate plans to manage such potential disasters.

First, Dr Ruti took me through the technological and analytical aspects of improving extreme weather forecasting. This includes advances in the resolution of the forecast models themselves, which enable the latest weather models to include physical processes which are critical to predicting extreme weather.

A good example (among many) of improved resolution making a difference to forecasting capability is models which allow for resolving convection processes (drawing moisture for hurricane generation at scales less than 10km spatially) to take place. The newest models are showing remarkable realism in their simulation of the potential evolution of severe storm events for up to 15 days. This additional forecast lead time and accuracy will become invaluable when planning evacuation in cities during hurricane season.

Another key component of improving forecasts is identifying what in weather patterns may indicate the onset of extremes. This includes the work of Dr Hannah Nissan, a researcher at the International Research Institute (IRI) for Climate and Society at Columbia University, New York. Dr Nissan mainly works with developing countries in Africa and South Asia to develop and improve extreme weather early warning systems. She is an expert at extending the forecast horizon of warnings by finding novel sources of predictability.

Dr Nissan and the IRI have researched the predictability of extreme heat in Bangladesh.2 Nissan has explored patterns of air transport and soil moisture, and found “heat waves are preceded by a characteristic wind pattern in the atmosphere, which can set itself up about a week to 10 days before a major heat wave.” Not only that, but reliable warnings up to 30 days in advance may be possible because as ‘soils are drier than normal for at least a month before a heat wave on average.” This will be essential to plan mid-term action plans in response to destructive heat waves.

Beyond improving modelling of weather and its extremes, challenges include data collection to power numerical and physical modelling of weather extremes. Data from the ground weather stations is the fuel for forecasting extreme weather. Developing countries like Bangladesh, however, suffer from a lack of essential weather measurement infrastructure. Dr Ruti of WWRP explains that “data, and real-time observations are a key challenge” for this purpose.

To address this challenge, Ruti said “weather data is being crowd sourced through third party networks using apps or portable weather measurement devices.” An example Ruti highlighted was of the use of mobile phones as a proxy for radar measurements to monitor and forecast rainfall. A pilot project by the French Institute for Development Research in Toulouse has demonstrated a proof-of-concept system in Burkina Faso, Niger, and Cameroon.3,4 This type of technology could emerge as especially important in parts of the world with poor ground-based weather monitoring infrastructure. With high penetration of mobile phones in most of the world,5 it is a promising example of where the next generation of weather data collection and assimilation into forecast models lies.

However, using data from public sources isn’t as simple as plugging it in to a forecasting system. Ruti warned me that “understanding the error characteristics of these data will be critical to using them effectively.” Prior to data post-processing using mobile phone data, the readings of rainfall could be two to three times the real value. Data needs to be trustworthy and processed correctly before it is used for forecast outcomes. Yet, the work is a positive move away from the expensive centralised government weather measurement projects in places which cannot necessarily afford to maintain them.

Crowd sourcing contributions in the aftermath of extreme weather is also a growing area. A leader in the field is Zooniverse, the world’s “largest and most popular platform for people-powered research.”6 The crowd sourcing of over 10,000 contributions to observing the devastation of Irma and Maria from satellite data has resulted in a damage analysis of buildings all over the Caribbean in just a few days, which would have traditionally taken a single researcher over a year. Partnering with the Machine Learning research group at Oxford, Zooniverse have built a powerful mechanism to catalogue damage from extreme weather events, which is invaluable for the clean-up and rebuilding phase.

Zooniverse continues to be involved with identifying the damage caused by the devastating hurricanes of summer 2017, as well as the historical classification of cyclones to help further the understanding of patterns and potential trends of cyclones under climate change. While these are two independent projects, they both serve the broad goal of increasing understanding and preparedness for any similar future events.

This kind of work attracts a wide range of contributors (based on a 2015 survey), with a balanced distribution of ages. Currently, those actively participating are mainly from English-speaking countries, as a combined 64% originate from the UK or the USA, with only 2% from developing countries. Many state that contributing to the projects on Zooniverse is fun, and that contributing to scientific progress is a strong part of the appeal.

Professor Brooke Simmons, Einstein fellow at UC San Diego, and leader of the Zooniverse Analysis Group, explains that the work users are doing on Zooniverse is a making a noticeable difference on the ground, with “really good feedback” from first responders, especially in response to Irma and Maria in 2017, where Zooniverse was called in to inspect the damage.

One such first responder, Rebekah Yore, of Rescue Global, an international Non-Governmental Organisation working to reduce disaster risk reduction around the world. Having previously worked on the response to Irma and Maria in 2017, Yore explains that Rescue Global could quickly find a set of personnel via Zooniverse whereby thousands of volunteers rapidly analysed pre- and post-storm satellite images to identify “specifically features of damage and hazard from infrastructure collapse, flooding and other causes”.

Once the volunteers’ work was done, the Machine Learning department at the University of Oxford would ‘run the responses through algorithms to filter anomalous results, improve the reliability of data, and produce heat maps of identified damage and features on the ground’. This became an essential component in Rescue Global’s humanitarian response, which was involved in determining which affected populations’ needs were the most critical, and which areas may have been more dangerous to traverse for teams deployed on the ground.

From a humanitarian response perspective, and particularly from the hurricane response in the Caribbean in 2017, Zooniverse allowed a great amount of accurate information to be generated extraordinarily quickly. Yore is quite clear that without Zooniverse, the first responders could not have had this kind of information to hand. Why was this information so useful? Yore sets the scene for what greets the those who are first to such disaster zones: “villages may have disappeared, there may be new, spontaneous groups of internally-displaced people. Bridges and access roads can also be destroyed, with flooding and landslides potentially blocking access”. The insights from Zooniverse are essential for both finding vulnerable populations while also protecting members of the rescue community.

Thus, arguably the most dramatic change to preparing for extreme weather is also how responses to them are managed. HIWeather has also identified endemic challenges of what a forecast should indicate, such as how to best spread effective warnings to a vulnerable community. Social media activity is a good way to analyse how effectively an extreme weather warning is being received and followed.7

While it is one thing to create experimental forecasting products; it is another to create something which decision-makers can use without requiring very specialised knowledge, often sorely lacking at Earth’s most vulnerable areas. Dr Joy Shumake-Guillemot leads the Joint Office for Climate and Health within the WMO, which specialises in translating forecasts into actions which local decision-makers can employ in extreme weather events.

Shumake-Guillemot recognizes how important it is to translate forecasting research into something useable for those on the ground: “the last-mile user (i.e. those who have to use the results for the purposes of minimising risk for vulnerable communities) in the ministry of health is often the neglected piece of the puzzle. Without building forecasting systems that factor in decision making, cutting-edge forecast products can so often become unused, especially in developing countries.”

Dr Ruti of WWRP also made clear to me that the key to saving lives and livelihoods is not only the forecasting technology, but also the methods of communicating the hazards to a community: ‘Better prediction and communication should go hand-in-hand’. We need to understand the ‘physical and social factors limiting the capability to communicate’, as well as understanding how to find better ways of forecasting.

A critical failure in the most devastating disaster to ever hit Myanmar, Cyclone Nargis in 2008, was that although the Indian Meteorological Office had predicted the extreme with four days’ notice,8 traditional methods of disseminating the warning (such as via TV and radio) did not reach isolated communities in the low-lying regions of the country, resulting in over 100,000 potentially avoidable deaths.

The link between decision makers and predictions often is at a disconnect, and this is the mid-term key to improving action on disaster forecasts. While events like Hurricane Harvey and others are truly terrible, they provide an opportunity to remind policy makers that it is critical we get the forecasting and communication coupling right. The future of forecasting includes targeted messaging to vulnerable populations, including the elderly, the young, prisoners, and labourers. Great gains can be made with existing technology and improved communication.

Of course, key steps forward in forecasting technology will occur over the next decade. But progress will not only develop in the models’ themselves, but also in the data collection for the models by crowd sourcing, and clarifying and communicating the forecasts to decision makers and then to vulnerable communities.

The future is full of extreme weather, but we can at least know a good deal more about it before it arrives.